Alphabet Sign Language Image Classification Using Deep Learning

Daroya, Rangel; Peralta, Daryl; Naval, Prospero C.

doi:10.1109/tencon.2018.8650241

Cited by 23 publications

(10 citation statements)

References 12 publications

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“…The resulting system achieved high accuracy, even when evaluating letters that had shared gestures. Daroya et al [117] used a CNN model to examine the performance of a framework they proposed. The experiment applied Alexnet (an effective CNN model) and altered a few parameters to adapt it to their dataset consisting of 28 × 28 pixel images.…”

Section: ) Deep Learning Techniquesmentioning

confidence: 99%

“…Some experiments have applied deep learning to classify RGB images. For instance, in [117], a CNN was used as an approach that can classify RGB images of static hand poses (representing a letter) associated with sign language. This method is based on DenseNet.…”

Section: ) Deep Learning Techniquesmentioning

confidence: 99%

“…A notable trend found was that almost all algorithms exhibited mixed performance from one sign to another, and in general, only a handful of confusing signs were typically re- [197] 2D and 3D skeletal representations, depth data [64] RGB [126] RGB, RGB-D Kinect [44] intensity camera , Kinect [39] RGB image, Kinect [76] RGBD [65] RGB Video [3] RGB [129] 6D IMU data [116] RGB [117] RGB [134] RGB videos [48] RGB, Kinect sponsible for a large portion of false recognitions. These frequent mistakes often persisted regardless of the classifier or training procedure, and were caused either by the similarity between hand gestures or other systemic factors.…”

Section: B Performance Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning for Sign Language Recognition: Current Techniques, Benchmarks, and Open Issues

Al‐Qurishi¹,

Khalid²,

Souissi³

2021

IEEE Access

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People with hearing impairments are found worldwide; therefore, the development of effective local level sign language recognition (SLR) tools is essential. We conducted a comprehensive review of automated sign language recognition based on machine/deep learning methods and techniques published between 2014 and 2021 and concluded that the current methods require conceptual classification to interpret all available data correctly. Thus, we turned our attention to elements that are common to almost all sign language recognition methodologies. This paper discusses their relative strengths and weaknesses, and we propose a general framework for researchers. This study also indicates that input modalities bear great significance in this field; it appears that recognition based on a combination of data sources, including vision-based and sensor-based channels, is superior to a unimodal analysis. In addition, recent advances have allowed researchers to move from simple recognition of sign language characters and words towards the capacity to translate continuous sign language communication with minimal delay. Many of the presented models are relatively effective for a range of tasks, but none currently possess the necessary generalization potential for commercial deployment. However, the pace of research is encouraging, and further progress is expected if specific difficulties are resolved.

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Section: ) Deep Learning Techniquesmentioning

confidence: 99%

Section: ) Deep Learning Techniquesmentioning

confidence: 99%

Section: B Performance Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning for Sign Language Recognition: Current Techniques, Benchmarks, and Open Issues

Al‐Qurishi¹,

Khalid²,

Souissi³

2021

IEEE Access

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“…Sign Language as an alternative means of communication for individuals with hearing and speech impairments is very important. Research on the development of sign language recognition using artificial intelligence technology has been started since 1998 by S. Naidoo using traditional machine learning methods, namely the support vector machine (SVM) to classify South African Sign Language (SASL) [9]. The introduction of Sign Language in general involves several processes, namely segmentation, feature extraction and classification,…”

Section: Related Workmentioning

confidence: 99%

Classification of Finger Spelling American Sign Language Using Convolutional Neural Network

Marjusalinah

Samsuryadi²,

Buchari

2021

ComEngApp

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Sign language is a combination of complex hand movements, body postures, and facial expressions. However, only a limited number of people can understand and use it. A computer aid sign language recognition with finger spelling style utilizing a convolutional neural network (CNN) is proposed to reduce the burden. We compared two CNN architectures such as Resnet 50, and DenseNet 121 to classify the American sign language dataset. Several data splitting proportions were also tested. From the experimental result, it is shown that the Resnet 50 architecture with 80:20 data splitting for training and testing indicates the best performance with an accuracy of 0.999913, sensitivity 0.998966, precision 0.998958, specificity 0.999955, F1-score 0.999913, and error 0.0000898.

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“…Different hand gestures, facial expressions and body postures form grammatically-complete, highly-structured sign languages. Sign language recognition (SLR) can be categorized into three groups: recognition of alphabets [7,24,25], isolated words [13], or continuous sentences. In continuous SLR the input comprises a video containing a sequence of gestures and the desired output is a sequence of words complying to the grammar of that sign language [6,11,12].…”

Section: Introductionmentioning

confidence: 99%

Multi-phase Fine-Tuning: A New Fine-Tuning Approach for Sign Language Recognition

2022

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In this paper, we propose multi-phase fine-tuning for tuning deep networks from typical object recognition to sign language recognition (SLR). It extends the successful idea of transfer learning by fine-tuning the network’s weights over several phases. Starting from the top of the network, layers are trained in phases by successively unfreezing layers for training. We apply this novel training approach to SLR, since in this application, training data is scarce and differs considerably from the datasets which are usually used for pre-training. Our experiments show that multi-phase fine-tuning can reach significantly better accuracy in fewer training epochs compared to previous fine-tuning techniques

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Alphabet Sign Language Image Classification Using Deep Learning

Cited by 23 publications

References 12 publications

Deep Learning for Sign Language Recognition: Current Techniques, Benchmarks, and Open Issues

Deep Learning for Sign Language Recognition: Current Techniques, Benchmarks, and Open Issues

Classification of Finger Spelling American Sign Language Using Convolutional Neural Network

Multi-phase Fine-Tuning: A New Fine-Tuning Approach for Sign Language Recognition

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